Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/146193
Authors: 
Kim, Kun Ho
Chao, Shih-Kang
Härdle, Wolfgang
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-024
Abstract: 
In this paper, we analyze the nonparametric part of a partially linear model when the covariates in parametric and non-parametric parts are subject to measurement errors. Based on a two-stage semi-parametric estimate, we construct a uniform con dence surface of the multivariate function for simultaneous inference. The developed methodology is applied to perform inference for the U.S. gasoline demand where the income and price variables are measured with errors. The empirical results strongly suggest that the linearity of the U.S. gasoline demand is rejected.
Subjects: 
Measurement error
Partially linear model
Regression calibration
Non-parametric function
Semi-parametric regression
Uniform con dence surface
Simultaneous inference
U.S. Gasoline demand
Non-linearity
JEL: 
C12
C13
C14
Document Type: 
Working Paper

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